1. Technical Field
The present invention relates in general to improved grid computing and in particular to automated bidding for virtual job requests within a grid environment. Still more particularly, the present invention relates to responding to virtual grid job requests for grid resources by calculating the capacity and cost of grid resources to handle the workload requirements for the virtual requests, where a bid for handling the virtual job request can be generated based on the capacity and cost of the grid environment to handle the workload requirements.
2. Description of the Related Art
Ever since the first connection was made between two computer systems, new ways of transferring data, resources, and other information between two computer systems via a connection continue to develop. In typical network architectures, when two computer systems are exchanging data via a connection, one of the computer systems is considered a client sending requests and the other is considered a server processing the requests and returning results. In an effort to increase the speed at which requests are handled, server systems continue to expand in size and speed. Further, in an effort to handle peak periods when multiple requests are arriving every second, server systems are often joined together as a group and requests are distributed among the grouped servers. Multiple methods of grouping servers have developed such as clustering, multi-system shared data (sysplex) environments, and enterprise systems. With a cluster of servers, one server is typically designated to manage distribution of incoming requests and outgoing responses. The other servers typically operate in parallel to handle the distributed requests from clients. Thus, one of multiple servers in a cluster may service a client request without the client detecting that a cluster of servers is processing the request.
Typically, servers or groups of servers operate on a particular network platform, such as Unix or some variation of Unix, and provide a hosting environment for running applications. Each network platform may provide functions ranging from database integration, clustering services, and security to workload management and problem determination. Each network platform typically offers different implementations, semantic behaviors, and application programming interfaces (APIs).
Merely grouping servers together to expand processing power, however, is a limited method of improving efficiency of response times in a network. Thus, increasingly, within a company network, rather than just grouping servers, servers and groups of server systems are organized as distributed resources. There is an increased effort to collaborate, share data, share cycles, and improve other modes of interaction among servers within a company network and outside the company network. Further, there is an increased effort to outsource nonessential elements from one company network to that of a service provider network. Moreover, there is a movement to coordinate resource sharing between resources that are not subject to the same management system, but still address issues of security, policy, payment, and membership. For example, resources on an individual's desktop are not typically subject to the same management system as resources of a company server cluster. Even different administrative groups within a company network may implement distinct management systems.
The problems with decentralizing the resources available from servers and other computing systems operating on different network platforms, located in different regions, with different security protocols and each controlled by a different management system, has led to the development of Grid technologies using open standards for operating a grid environment. Grid environments support the sharing and coordinated use of diverse resources in dynamic, distributed, virtual organizations. A virtual organization is created within a grid environment when a selection of resources, from geographically distributed systems operated by different organizations with differing policies and management systems, is organized to handle a job request.
One important application of a grid environment is that companies implementing an enterprise computing environment can access external grid computing “farms”. Sending jobs to a grid computing farms is one way to outsource job execution. The grid computing farms may include groups of grid resources accessible for executing grid jobs received from multiple customers.
A limitation of current grid computing farms is that the process for attaining bids from multiple grid computing farms is time consuming and inefficient. In particular, a customer needing to send jobs to a grid computing farm will typically want the least costly grid computing farm from among multiple available grid farm providers to process the grid jobs. However, to determine the most competitive bid from among the multiple available grid farm providers, a customer must contact a representative of each grid computing farm, provide a description of the type of grid resources needed and receive a bid from the representative of each grid farm. Requiring a customer to contact a representative of each grid computing farm and go through a bidding process is inefficient for the customer and for the grid computing farm vendor.
Therefore, in view of the foregoing, it would be advantageous to provide a method, system, and program for receiving virtual grid job requests and automatically calculating a bid for performing the grid job, such that the consumer and the grid resource vendor can more efficiently determine whether a particular grid environment is able to handle a virtual grid job request and provide on the spot prices for use of a grid environment. In particular, it would be advantageous to provide a method, system, and program for calculating the workload associated with a virtual grid job request so that an accurate determination of the ability of the grid environment to handle a virtual grid job request is quickly determines and a price can be calculated based on the estimated workload of the virtual grid job request.